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December, 2013

Microsoft Research Connections Blog

The Microsoft Research Connections blog shares stories of collaborations with computer scientists at academic and scientific institutions to advance technical innovations in computing, as well as related events, scholarships, and fellowships.

December, 2013

  • Microsoft Research Connections Blog

    Cancer research benefits from NodeXL network graph analysis

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    A diagnosis of cancer can be particularly foreboding for any patient. However, new treatments become possible as we learn more about the disease, and the application of research techniques more commonly found in the social sciences are now providing new insights.

    Although medical investigators have been studying cancer for decades, only recently have they focused attention on micro RNAs (miRNAs), small RNA molecules that affect gene regulation and probably many other biological processes. Studies are now underway to learn if alterations in miRNA expression profiles can be used to identify drivers in both colorectal and pancreatic cancers.

    To study miRNA expression profiles and their relation to these cancers, researcher Tommaso Mazza in the Bioinformatics research unit at Italy’s Casa Sollievo della Sofferenza Research Hospital and his colleagues paired normal and tumor tissue samples from patients with colorectal or pancreatic cancer and determined the relative levels of miRNA expression in each sample. They then subjected the data to complex statistical analysis to determine which miRNAs appear to be affected in each cancer.

    Once the set of miRNAs affected in each cancer type was identified, the researchers applied analyses more commonly seen in the social sciences to construct and analyze the network of interactions between them. To do this, Dr. Mazza and his colleagues built a standalone application in C# utilizing the NodeXL network graph-analysis platform. Analyses of these graphs revealed that each of these cancers is associated with a unique pattern of changes specific to the tissue in which it occurs, and that certain key miRNAs could be tied to biochemical pathways in the cell, some previously known to be associated with cancer—but some that are new discoveries, to be validated in future research. This work also demonstrates the new insights that analytical techniques common in one area of science can bring when applied in a different field.


    NodeXL includes an Excel template for easy manipulation of graph data.NodeXL includes an Excel template for easy manipulation of graph data.

    NodeXL is a free, open-source template for Microsoft Excel that displays and analyzes graphs by utilizing a custom Windows Presentation Foundation (WPF) control. It can be invaluable whenever you want to explore network graphs. NodeXL can import and export graphs in GraphML, Pajek, UCINET, and matrix formats and can be configured to import and analyze networks from social networking sites, email interactions from Microsoft Exchange, or graphs of web hyperlinks. If you would like to learn more, the NodeXL webpage has a programmer discussion forum and a method to download the latest class libraries.

    The research of Dr. Mazza and his colleagues on miRNA expression profiles was published in PLOS ONE (an international, peer-reviewed, open-access, online publication). You can access the paper on the PLOS website.

    Simon Mercer, Director of Health and Wellbeing, Microsoft Research Connections

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    Scale out your research with virtual machines: Windows Azure webinar

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    Watch the webinar: Virtual Machines for Research on Windows AzureResearchers often ask us, “What’s the easiest way to get started with cloud computing?” Cloud computing can seem daunting, but Windows Azure makes it easier than ever to analyze and manage large datasets in the cloud. Want to know more? Then please join us tomorrow (December 4 or December 5, depending on your time zone) for the next installment in our webinar series that explains what, why, and how of the cloud can free you from limited computing resources and the expense of hardware procurement.

    One of the best ways to get going is to use one of the pre-configured “science-in-a-box” Linux virtual machines. These great little packages bring together all of the tools you need, so that you can deploy them in the cloud with just a few mouse clicks. You can grab these from our VM Depot, where there are dozens to choose from, including BioLinux and a Data Science VM with IPython, as well as big data tools such as Kafka and STORM. In tomorrow’s webinar, we’ll walk you through how to create Linux and Windows VMs for scientific applications, both from VM Depot and from scratch, so you can build your own VM tuned for your research. Once you’ve built your VM, you can literally spin up hundreds of them to run those big calculations needed to meet your publication deadline—that’s the power of the cloud in action.

    Windows Azure virtual machines deployed from VM Depot: Science-in-a-Box
    Windows Azure virtual machines deployed from VM Depot: Science-in-a-Box

    We’re delighted to present the webinar twice, at 8:00 A.M. Pacific Time (that’s 4:00 P.M. GMT December 4, morning for Western Hemisphere researchers and late afternoon for those in Europe and Africa), and at 6:00 P.M. Pacific Time (2:00 A.M. GMT December 5, and after breakfast in Asia). Wherever you are on the globe, we hope you join us online for this informative session.

    To complement the webinars, we’ve created some getting started guides at the Windows Azure for Research site to provide a deeper dive. So please join us for the webinar series and dig deeper with our technical papers to liberate your research by reaching for the cloud.

    Kenji Takeda, Solutions Architect and Technical Manager, Microsoft Research Connections EMEA

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    Microsoft Research Asia funds cloud computing for urban studies

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    The world is becoming more urban. The movement of populations from rural to city life is nothing new in the developed countries of Europe and North America, but it has greatly accelerated in the rapidly developing countries of Asia. In China, for example, the percentage of urban dwellers has swelled from less than 30 percent in 1980 to over 50 percent—and growing—today. Given the rapid growth of cities in the developing world, the United Nations estimated that in 2008, for the first time in history, more than half of the world’s population resided in urban areas.

    Rapid urbanization poses challenges, as growing cities strive to deliver services, maintain a safe and healthful environment, and promote a vibrant economy. Meetings these challenges requires actions based on the collection, analysis, and modeling of reliable data, a need that has given rise to the field of urban informatics. Think of it as the big data of big cities.

    Using big data to tackle big challenges cities face
    Using big data to tackle big challenges cities face

    Crunching big data is one of the strengths of cloud computing, and Windows Azure, the cloud-computing platform from Microsoft, offers tremendous potential in urban informatics. With this in mind, earlier this year Microsoft Research Asia issued an invitation for proposals that use Windows Azure to accelerate urban informatics, with the winning proposals receiving grants that support the research for at least a year.

    After evaluating 60 proposals from 34 Asian universities and institutions, the Microsoft Research Asia team has selected 25 projects for funding. The winning projects cover a broad spectrum of urban informatics research, from enhancing transportation, to mapping city noise, to preserving the privacy of urbanites and even tracking social happiness. The winning projects come from institutions throughout East Asia, including those in China, Hong Kong, Japan, Korea, and Singapore. All results arising from the funded projects will be broadly available, either in the public domain or under a non-restrictive license that allows modification and redistribution without significant restrictions or conditions.

    We’re delighted to be funding these important studies, the results of which, we hope, will make city life more livable in years ahead.

    —Kangping Liu, Senior Manager, Microsoft Research Connections Asia

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    Funded projects

    • Guangzhong Sun, University of Science and Technology of China (China): Smart campus construction based on rich campus datasets
    • Han-Lim Choi, Korea Advanced Institute of Science and Technology (Korea): Scalable Gaussian Process-Enabled Bayesian Inference for Sensor Networks in Smart Buildings
    • Hojung Cha, Yonsei University (Korea): Development of a CrowdSensing Framework for Inducing User Participation in Urban Environments
    • Hong Cheng, Chinese University of Hong Kong (Hong Kong): Optimal Point of Interest Routing in a Urban Environment
    • Huayi Wu, Wuhan University (China): Collaborative Geoprocessing on Windows Azure
    • Hwasoo Yeo, Korea Advanced Institute of Science and Technology (Korea): Smart phone based Urban Travel Pattern Analysis and Prediction with Online Traffic Simulator
    • Janny Leung, Chinese University of Hong Kong (Hong Kong): Back to the Future: Sense-and-Respond Public Transit for Historic Urban Centres
    • Jitao Sang, Institute of Automation, Chinese Academy of Sciences (China): Cyber-Physical Footprint Association for Urban Computing
    • Joon Heo, Yonsei University (Korea): Does ‘Gangnam Style’ really exist? Answers from data science perspective
    • Jun Ma, Shandong University (China): Urban lifestyles Detection based on Big Heterogeneous Human Behavioral Data
    • Kohei Matsumura, Future University of Hakodate (Japan): A multimodal approach for in-car conversation sharing
    • Lei Chen, Hong Kong University of Science and Technology (Hong Kong): Urban Traffic Monitoring-based Mobile Crowdsourcing
    • Lei Zou, Peking University (China): Graph Data Management in Urban Computing
    • Long Quan, Hong Kong University of Science and Technology (Hong Kong): Large-scale Three-dimensional Urban Reconstruction
    • Rajesh Balan, Singapore Management University (Singapore): Building a Practical Location System for Tracking Consumer Movement in Indoor Public Spaces
    • Soobin Lee, Korea Advanced Institute of Science and Technology (Korea): Waste Management Planning based on Multi-Sensor Data Fusion
    • Tai-Quan Peng, Nanyang Technological University (Singapore): Tracking Dynamics of Social Happiness on Twitter: a Multi-level Study
    • Victor Li, University of Hong Kong (Hong Kong): A Big Data Stream Processing Solution for Hidden Causality Detection of Urban Dynamics
    • Xiaokui Xiao, Nanyang Technological University (Singapore): Preserving Individual Privacy in Urban Informatics
    • Xueming Qian, Xi'an Jiaotong University (China): Schedule travel life by exploring spectrums of social user and city services
    • Yanmin Zhu, Shanghai Jiao Tong University (China): NoiseSense: Crowdsourcing-Based Urban Noise Mapping with Smartphones
    • Ying-Qing Xu, Tsinghua University (China), Stephen Jia Wang, Monash University (Australia): Intelligent Sustainable Navigation Services (ISUNS): To Enhance Eco-Efficiency for Urban Transportation by Adopting Clouds and Pervasive Computing Technologies
    • Yuguo LI, University of Hong Kong (Hong Kong): SmartComfort—Use of smartphone and cloud technologies for building thermal comfort, and ventilation and health studies in megacities
    • Zhiwen Yu, Northwestern Polytechnical University (China): Understanding City Interest and Sentiment Leveraging Crowdsourced Digital Footprints from LBSNs
    • Zongjian He, Tongji University (China): Community Sensing-Based Green Driving System Using Smartphones

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